Improved methods for predicting peptide binding affinity to MHC class II molecules. Issue 3 (6th February 2018) - Jensen, Kamilla Kjærgaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A.; Yan, Zhen; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten
Summary: Major histocompatibility complex class II (MHC‐II) molecules are expressed on the surface of professional antigen‐presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC‐II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T‐cell epitopes. We here present updated versions of two MHC–II–peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC–peptide binding affinity data obtained from the Immune Epitope Database covering HLA‐DR, HLA‐DQ, HLA‐DP and H‐2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available atwww.cbs.dtu.dk/services/NetMHCII-2.3 andwww.cbs.dtu.dk/services/NetMHCIIpan-3.2 . Abstract : Predicting peptide binding affinity to MHC‐II is important for the identification of T‐cell epitopes. This paper presents updated versions for two popular MHC‐II peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. The updated methods, trained on a new data set of over 130 000 data points covering HLA‐DR, HLA‐DQ, HLA‐DP and H‐2 molecules, showed an improved performance compared with the previous versions.